Interested in this AI/ML Engineer role at Tory Burch?
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About This Role
We are an American luxury lifestyle brand, founded in 2004. Anchored in the casual elegance of American sportswear, Tory’s design philosophy is defined by effortless silhouettes, innovative materials, eclectic juxtapositions of color, and the tension of past and present. The collections include ready-to-wear, handbags, footwear, accessories, jewelry, home and beauty.
Empowering women is the company’s guiding principle, expressed through Tory’s collections and reflected in the company culture as well as the work of the Tory Burch Foundation. Established in 2009, the Foundation provides women entrepreneurs in the United States with access to capital, education and community.
You are seeking a work environment where people are encouraged to dream, explore, discover and, as important, laugh together. If you’re prepared to work hard, create impact, and have fun while doing it, we would love to have you join #TeamTory. Apply today!
Life @ToryBurch is Special Because:
When you join us, you’re joining a global, purpose-led company on an exciting growth journey with an amazing culture and great benefits.
Our culture is welcoming and inclusive * - everyone is empowered to make a difference.
- We have the best team in the world and believe in paying competitively and rewarding for high performance.
- Your overall well-being is important to us; we offer generous benefits to help you take care of your mental and physical health, create financial security, and achieve wellness in all areas of your life.
- We love seeing our employees wear our beautiful collections. You’ll receive a generous employee discount and access to exclusive sample sales.
- We are invested in your professional growth – you’ll have access to free executive coaching on-demand.
- We believe in the importance of giving back and you’ll have many opportunities to do just that through the Tory Burch Foundation and paid volunteer days.
This Role is Tailor-Made for You Because:
You are passionate about using data, analytics, automation, and artificial intelligence to solve complex business challenges and improve operational performance. You enjoy partnering across business and technology teams to translate strategic priorities into scalable reporting, process improvement, and AI-enabled solutions. You thrive balancing strategic thinking with hands-on execution, leveraging data and emerging technologies to drive visibility, productivity, and decision-making across the organization. A sense of humor is a plus!A Day in the Life:
The typical day is… atypical.
Key Responsibilities:
Operations Intelligence & Business Solutions
- Serve as the primary liaison among business stakeholders, Technology, and data teams, ensuring that business needs are accurately translated into scalable reporting, analytics, automation, and AI-enabled solutions.
- Support the Sr. Director, Operations Reporting & Analytics in shaping the department’s reporting and AI roadmap, backlog prioritization, adoption, and lifecycle management of analytics, automation, and AI solutions.
- Own the business-side execution of approved changes, translating business requirements and Technology direction into usable reporting, analytics, automation, and AI-enabled solutions.
- Develop and maintain business data models, reporting frameworks, metric definitions, information hierarchies, and business rules that support reporting and analytics.
- Document data flows, source systems, business rules, and reporting dependencies to improve transparency and reduce technical debt within the department.
- Identify gaps within existing data and reporting environments and recommend scalable solutions that improve data quality, operational visibility, and long-term department capabilities.
- Participate in Logistics & Distribution capability planning while maintaining clear ownership of operations reporting, analytics, automation, and AI deliverables.
AI, Automation & Digital Innovation
- Identify and implement AI, automation, and intelligent workflow opportunities that streamline Logistics & Distribution processes and improve operational productivity.
- Build, configure, and maintain Copilot agents, Power Platform solutions, automations, and other AI-enabled business tools within approved platforms and standards.
- Evaluate and prioritize Logistics & Distribution AI use cases based on business value, operational impact, and scalability.
- Support responsible AI governance, adoption, training, and value realization within the department.
- Develop metrics that measure productivity gains, cost savings, adoption, and business outcomes from automation and AI investments.
- Partner with Technology and business teams to operationalize governed AI solutions at scale.
Analytics, Reporting & Insights
- Design, develop, and maintain executive dashboards, KPI scorecards, and operational reporting solutions that enable data-driven decision-making across Logistics & Distribution as well as Supply Chain, Retail, and Finance partners.
- Translate stakeholder requirements into a prioritized pipeline of reporting and analytics deliverables.
- Develop scalable Tableau and Power BI solutions that support self-service analytics and leadership decision-making.
- Perform trend, performance, and root-cause analyses to identify risks and improvement opportunities.
- Create leadership-ready visualizations, presentations, and business insights that support strategic and operational decisions.
- Automate recurring reporting and expand timely access to reliable business insights across the organization.
Process Improvement & Operational Excellence
- Lead cross-functional process improvements that increase efficiency, scalability, and operational performance.
- Translate analytical findings into process recommendations and implementation plans.
- Develop business cases, ROI analyses, and investment recommendations for technology and process improvement initiatives.
Strategic Enablement & Innovation
- Research emerging technologies and evaluate their applicability to current and future business challenges.
- Conduct proof-of-concept efforts and pilot approved capabilities that drive competitive advantage.
- Establish reusable methods and best practices that support the scalable delivery of analytics and AI capabilities.
To Land This Role:
- Bachelor's degree in Business, Supply Chain, Information Systems, Data Analytics, Computer Science, Engineering, or a related field.
- 10+ years of experience in analytics, business intelligence, reporting, automation, process improvement, or related business technology disciplines.
- Strong experience developing and supporting business intelligence, reporting, dashboarding, or analytics solutions.
- Hands-on experience with Tableau, Power BI, Excel, and SQL or similar data querying tools.
- Experience gathering business requirements and translating them into technical specifications, data requirements, solution designs, or reporting frameworks.
- Working knowledge of data modeling, reporting architecture, systems integration concepts, data governance, and business rules management.
- Experience partnering with business stakeholders, Technology, and cross-functional teams to deliver scalable business solutions.
- Strong communication and presentation skills with the ability to explain technical concepts to non-technical audiences.
- Demonstrated ability to balance strategic solution design with hands-on execution and delivery.
- Experience with Google Cloud Platform, Looker, or similar data and visualization platforms.
- Experience with Microsoft Copilot, Copilot Studio, Power Platform, or Power Automate is preferred.
- Experience developing or supporting AI, automation, or intelligent workflow solutions.
- Retail, Supply Chain, Distribution, Logistics, or Operations experience is preferred.
- Experience supporting technology implementations, data initiatives, or digital transformation programs.
Why You'll Want to Join Our Team:
The Global Transportation Team connects our brand to our customers by overseeing the delivery of our products around the world. Relying on a diverse network of logistics partners, we move product efficiently and effectively from manufacturing centers to distribution centers and eventually to our customers. No shipment is too big or too small, we handle it all, delivering on time and at the right price. If you are a detail-oriented problem-solver interested in guiding our products on their journey, come join us!How We Work Together
- Adaptable – We change before we have to
- Entrepreneurial – We own it
- Collaborative – There’s no “I” in Tory
- Client & Brand Focused – We put ourselves in Tory’s shoes
- Live the Values – We show up for each other
- Functional Expertise – We’re constantly learning and growing
#TeamTory Values
We show up with honesty & kindness, act with integrity & compassion, work with passion & humility and lead with excellence & humor.
Compensation Range
The compensation range for this position is 140,000.00 USD - 180,000.00 USD. Our offer will be based on your relevant experience and work location. Benefits Information
We offer a generous set of benefits to help you take care of your health, create financial security, and achieve wellness in all areas of your life. Here are highlights of key benefits available to all eligible Tory Burch team members.
Equal Employment Opportunity Statement
Tory Burch LLC is an Equal Employment Opportunity employer and provides equal opportunities to all employees and applicants without regard to an individual’s age, race, creed, color, religion, national origin, sex (including pregnancy) or sexual orientation, gender expression, military status, marital status, genetic predisposition or carrier status, disability or membership in any other protected class under applicable law. Likewise, we will consider qualified applicants with criminal histories for employment in a manner consistent with the requirements of the Los Angeles Fair Chance Initiative for Hiring, Ordinance No. 184652. Pursuant to the San Francisco Fair Chance Ordinance, we will consider qualified applicants with arrest and conviction records for employment.
Disability Accommodation
Tory Burch is committed to providing reasonable accommodations to applicants and employees with disabilities. Please tell us if you require a reasonable accommodation to apply for a job or to perform your job. Examples of reasonable accommodation include making a change to the application process or work procedures, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment. If you require assistance or an accommodation with the hiring process, please contact [email protected].
Salary Context
This $140K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Tory Burch, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($160K) sits 25% below the category median. Disclosed range: $140K to $180K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Tory Burch AI Hiring
Tory Burch has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Jersey City, NJ, US. Compensation range: $180K - $180K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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